{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pinecone-io/examples/blob/master/learn/search/faiss-ebook/hnsw-faiss/hnsw_faiss.ipynb) [![Open nbviewer](https://raw.githubusercontent.com/pinecone-io/examples/master/assets/nbviewer-shield.svg)](https://nbviewer.org/github/pinecone-io/examples/blob/master/learn/search/faiss-ebook/hnsw-faiss/hnsw_faiss.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# HNSW in Faiss\n",
    "\n",
    "In this notebook we will explore the implementation of HNSW in Facebook AI Similarity Search (Faiss).\n",
    "\n",
    "First we import Faiss and our data (we will use the [Sift1M dataset](https://gist.github.com/jamescalam/928a374b85daffa49a565f3dc18d059c#file-get_sift1m-ipynb) as usual)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import faiss\n",
    "import numpy as np\n",
    "\n",
    "# now define a function to read the fvecs file format of Sift1M dataset\n",
    "def read_fvecs(fp):\n",
    "    a = np.fromfile(fp, dtype='int32')\n",
    "    d = a[0]\n",
    "    return a.reshape(-1, d + 1)[:, 1:].copy().view('float32')\n",
    "\n",
    "# 1M samples\n",
    "xb = read_fvecs('../../sift/sift_base.fvecs')\n",
    "# queries\n",
    "xq = read_fvecs('../../sift/sift_query.fvecs')[0].reshape(1, -1)\n",
    "xq_full = read_fvecs('../../sift/sift_query.fvecs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<faiss.swigfaiss.HNSW; proxy of <Swig Object of type 'faiss::HNSW *' at 0x7fe828821630> >\n"
     ]
    }
   ],
   "source": [
    "# setup our HNSW parameters\n",
    "d = 128  # vector size\n",
    "M = 32\n",
    "efSearch = 32  # number of entry points (neighbors) we use on each layer\n",
    "efConstruction = 32  # number of entry points used on each layer\n",
    "                     # during construction\n",
    "\n",
    "index = faiss.IndexHNSWFlat(d, M)\n",
    "print(index.hnsw)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before building the index with `index.add` the HNSW structure is empty:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the HNSW index starts with no levels\n",
    "index.hnsw.max_level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([], dtype=int64)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# and levels (or layers) are empty too\n",
    "levels = faiss.vector_to_array(index.hnsw.levels)\n",
    "np.bincount(levels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can set the `efConstruction` and `efSearch` parameters, only `efConstruction` must be set before building the index. `efSearch` only affects search time behavior."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "index.hnsw.efConstruction = efConstruction\n",
    "index.hnsw.efSearch = efSearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "index.add(xb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have added our data (and built the index) we will see that the HNSW structure has been populated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# after adding our data we will find that the level\n",
    "# has been set automatically\n",
    "index.hnsw.max_level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([     0, 968746,  30276,    951,     26,      1], dtype=int64)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# and levels (or layers) are now populated\n",
    "levels = faiss.vector_to_array(index.hnsw.levels)\n",
    "np.bincount(levels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can even find which vector has been selected as our entry point (and change it too if you want with `index.hnsw.entry_point = int`, although this is probably a bad idea)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "118295"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index.hnsw.entry_point"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `HNSW::set_default_probas` function (from [HNSW.cpp](https://github.com/facebookresearch/faiss/blob/main/faiss/impl/HNSW.cpp))calculates the number of neighbors (in total) a vertex will have across the calculated number of layers. We find that Faiss' implementation does not use *M_max* or *M_max0* directly, but instead uses `M` to set these values. *M_max* is set to `M`, and *M_max* is set to `2*M`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_default_probas(M: int, m_L: float):\n",
    "    nn = 0  # set nearest neighbors count = 0\n",
    "    cum_nneighbor_per_level = []\n",
    "    level = 0  # we start at level 0\n",
    "    assign_probas = []\n",
    "    while True:\n",
    "        # calculate probability for current level\n",
    "        proba = np.exp(-level / m_L) * (1 - np.exp(-1 / m_L))\n",
    "        # once we reach low prob threshold, we've created enough levels\n",
    "        if proba < 1e-9: break\n",
    "        assign_probas.append(proba)\n",
    "        # neighbors is == M on every level except level 0 where == M*2\n",
    "        nn += M*2 if level == 0 else M\n",
    "        cum_nneighbor_per_level.append(nn)\n",
    "        level += 1\n",
    "    return assign_probas, cum_nneighbor_per_level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0.96875,\n",
       "  0.030273437499999986,\n",
       "  0.0009460449218749991,\n",
       "  2.956390380859371e-05,\n",
       "  9.23871994018553e-07,\n",
       "  2.887099981307982e-08],\n",
       " [64, 96, 128, 160, 192, 224])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "assign_probas, cum_nneighbor_per_level = set_default_probas(\n",
    "    32, 1/np.log(32)\n",
    ")\n",
    "assign_probas, cum_nneighbor_per_level"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we return `assign_probas` which is the probability of inserting a vertex at that layer (higher for lower layers), which is carried out by another function called `HNSW::random_level`, which looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# this is copy of HNSW::random_level function\n",
    "def random_level(assign_probas: list, rng):\n",
    "    # get random float from 'r'andom 'n'umber 'g'enerator\n",
    "    f = rng.uniform() \n",
    "    for level in range(len(assign_probas)):\n",
    "        # if the random float is less than level probability...\n",
    "        if f < assign_probas[level]:\n",
    "            # ... we assert at this level\n",
    "            return level\n",
    "        # otherwise subtract level probability and try again\n",
    "        f -= assign_probas[level]\n",
    "    # below happens with very low probability\n",
    "    return len(assign_probas) - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([968821,  30170,    985,     23,      1])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chosen_levels = []\n",
    "rng = np.random.default_rng(12345)\n",
    "for _ in range(1_000_000):\n",
    "    chosen_levels.append(random_level(assign_probas, rng))\n",
    "np.bincount(chosen_levels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need *a lot* of vectors for any of them to be assigned to the top-level layer *5* - using our `1_000_000` sample size we actually don't return any at this higher level, and in fact only return one vertex at level *4*.\n",
    "\n",
    "If we wanted, we could increase the likelihood of insertion at higher layers by *decreasing* or *increasing* the *level multiplier* `m_L`, but the difference is minor, very random (probability could shift up/down), and also changes the calculated optimal number of layers, so it is not worth changing unless you have a specific reason for doing so - and changing this isn't supported in Faiss (at least from the Python wrapper). We can access the `set_default_probas` function as we will see below, but on modifying the values we will see no impact.\n",
    "\n",
    "First let's see what our current and expected values would be using our Python implementation, when using `M == 32`, Faiss will set `m_L` as `1/log(M)`, which leaves us with `0.2885`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.28853900817779266"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1/np.log(32)  # the previous value we used for m_L"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Changing `M` and `m_L` using our Python implementation outputs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0.9999850546614752, 1.4945115161637832e-05], [64, 96])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set_default_probas(32, 0.09)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Over in Faiss, we can view the population of each level using our *'default'* implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([     0, 968746,  30276,    951,     26,      1])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "levels = faiss.vector_to_array(index.hnsw.levels)\n",
    "np.bincount(levels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's try using `index.hnsw.set_default_probas` to change the level structure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "del index\n",
    "index = faiss.IndexHNSWFlat(d, 32)\n",
    "index.hnsw.set_default_probas(32, 0.09)  # HNSW::set_default_probas(int M, float levelMult)\n",
    "index.hnsw.efConstruction = efConstruction\n",
    "index.add(xb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And if we print out the distribution of vertices across levels, we will see there is no difference - so modifying `m_L` (at least using Python), does not have any effect."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([     0, 968746,  30276,    951,     26,      1], dtype=int64)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "levels = faiss.vector_to_array(index.hnsw.levels)\n",
    "np.bincount(levels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let's validate that `m_L` values ~0 produce a single layer HNSW graph (eg a NSW graph):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([1.0], [64])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "assign_probas, cum_nneighbor_per_level = set_default_probas(32, 0.0000001)\n",
    "assign_probas, cum_nneighbor_per_level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "chosen_levels = []\n",
    "rng = np.random.default_rng(12345)\n",
    "for _ in range(1_000_000):\n",
    "    chosen_levels.append(random_level(assign_probas, rng))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1000000], dtype=int64)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.bincount(chosen_levels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Faiss also always ensures that at least one vertex is included at the highest level, as we can see by creating a small index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "del index\n",
    "index = faiss.IndexHNSWFlat(d, 32)\n",
    "index.hnsw.efConstruction = efConstruction\n",
    "index.add(xb[:1_000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0, 974,  25,   1], dtype=int64)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "levels = faiss.vector_to_array(index.hnsw.levels)\n",
    "np.bincount(levels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Testing Faiss Parameters\n",
    "\n",
    "We've worked through *a lot* of the implementation detail in Faiss, let's now take a look at how different parameters can affect the performance of our index.\n",
    "\n",
    "We have two index construction parameters that can be modified, `M` and `efConstruction`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "recall_idx = []\n",
    "\n",
    "index = faiss.IndexFlatL2(d)\n",
    "index.add(xb)\n",
    "D, recall_idx = index.search(xq_full[:1000], k=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "def get_memory(index):\n",
    "    faiss.write_index(index, './temp.index')\n",
    "    file_size = os.path.getsize('./temp.index')\n",
    "    os.remove('./temp.index')\n",
    "    return file_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/5 [00:00<?, ?it/s]2\n",
      "100%|██████████| 5/5 [00:30<00:00,  6.10s/it]\n",
      "  0%|          | 0/5 [00:00<?, ?it/s]4\n",
      "100%|██████████| 5/5 [00:53<00:00, 10.74s/it]\n",
      "  0%|          | 0/5 [00:00<?, ?it/s]8\n",
      "100%|██████████| 5/5 [01:17<00:00, 15.47s/it]\n",
      "  0%|          | 0/5 [00:00<?, ?it/s]16\n",
      "100%|██████████| 5/5 [01:31<00:00, 18.20s/it]\n",
      "  0%|          | 0/5 [00:00<?, ?it/s]32\n",
      "100%|██████████| 5/5 [02:57<00:00, 35.42s/it]\n",
      "  0%|          | 0/5 [00:00<?, ?it/s]64\n",
      "100%|██████████| 5/5 [03:50<00:00, 46.11s/it]\n",
      "  0%|          | 0/5 [00:00<?, ?it/s]128\n",
      "100%|██████████| 5/5 [04:46<00:00, 57.39s/it]\n",
      "  0%|          | 0/5 [00:00<?, ?it/s]256\n",
      "100%|██████████| 5/5 [05:36<00:00, 67.35s/it]\n",
      "  0%|          | 0/5 [00:00<?, ?it/s]512\n",
      "100%|██████████| 5/5 [06:26<00:00, 77.27s/it]\n",
      "  0%|          | 0/5 [00:00<?, ?it/s]2\n",
      "100%|██████████| 5/5 [00:30<00:00,  6.06s/it]\n",
      "  0%|          | 0/5 [00:00<?, ?it/s]4\n",
      "100%|██████████| 5/5 [00:53<00:00, 10.65s/it]\n",
      "  0%|          | 0/5 [00:00<?, ?it/s]8\n",
      "100%|██████████| 5/5 [01:16<00:00, 15.34s/it]\n",
      "  0%|          | 0/5 [00:00<?, ?it/s]16\n",
      "100%|██████████| 5/5 [01:31<00:00, 18.23s/it]\n",
      "  0%|          | 0/5 [00:00<?, ?it/s]32\n",
      "100%|██████████| 5/5 [02:56<00:00, 35.22s/it]\n",
      "  0%|          | 0/5 [00:00<?, ?it/s]64\n",
      "100%|██████████| 5/5 [03:51<00:00, 46.25s/it]\n",
      "  0%|          | 0/5 [00:00<?, ?it/s]128\n",
      "100%|██████████| 5/5 [04:46<00:00, 57.31s/it]\n",
      "  0%|          | 0/5 [00:00<?, ?it/s]256\n",
      "100%|██████████| 5/5 [05:36<00:00, 67.29s/it]\n",
      "  0%|          | 0/5 [00:00<?, ?it/s]512\n",
      "100%|██████████| 5/5 [06:25<00:00, 77.01s/it]\n",
      "  0%|          | 0/5 [00:00<?, ?it/s]2\n",
      "100%|██████████| 5/5 [00:30<00:00,  6.13s/it]\n",
      "  0%|          | 0/5 [00:00<?, ?it/s]4\n",
      "100%|██████████| 5/5 [00:52<00:00, 10.59s/it]\n",
      "  0%|          | 0/5 [00:00<?, ?it/s]8\n",
      "100%|██████████| 5/5 [01:16<00:00, 15.33s/it]\n",
      "  0%|          | 0/5 [00:00<?, ?it/s]16\n",
      "100%|██████████| 5/5 [01:30<00:00, 18.10s/it]\n",
      "  0%|          | 0/5 [00:00<?, ?it/s]32\n",
      "100%|██████████| 5/5 [02:56<00:00, 35.39s/it]\n",
      "  0%|          | 0/5 [00:00<?, ?it/s]64\n",
      "100%|██████████| 5/5 [03:50<00:00, 46.18s/it]\n",
      "  0%|          | 0/5 [00:00<?, ?it/s]128\n",
      "100%|██████████| 5/5 [04:46<00:00, 57.24s/it]\n",
      "  0%|          | 0/5 [00:00<?, ?it/s]256\n",
      "100%|██████████| 5/5 [05:36<00:00, 67.34s/it]\n",
      "  0%|          | 0/5 [00:00<?, ?it/s]512\n",
      "100%|██████████| 5/5 [06:26<00:00, 77.33s/it]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from tqdm.auto import trange\n",
    "from datetime import datetime\n",
    "\n",
    "results = pd.DataFrame({\n",
    "    'M': [],\n",
    "    'efConstruction': [],\n",
    "    'efSearch': [],\n",
    "    'recall@1': [],\n",
    "    'build_time': [],\n",
    "    'search_time': [],\n",
    "    'memory_usage': []\n",
    "})\n",
    "\n",
    "for epoch in range(3):\n",
    "    for M_bit in range(1, 10):\n",
    "        M = 2 ** M_bit\n",
    "        print(M)\n",
    "        for ef_bit in trange(1, 6):\n",
    "            efConstruction = 2 ** ef_bit\n",
    "            index = faiss.IndexHNSWFlat(d, M)\n",
    "            index.efConstruction = efConstruction\n",
    "            start = datetime.now()\n",
    "            index.add(xb)\n",
    "            build_time = (datetime.now() - start).microseconds\n",
    "            memory_usage = get_memory(index)\n",
    "            for efSearch in [2, 4, 8, 16, 32]:\n",
    "                index.efSearch = efSearch\n",
    "                start = datetime.now()\n",
    "                D, I = index.search(xq_full[:1000], k=1)\n",
    "                search_time = (datetime.now() - start).microseconds\n",
    "                recall = sum(I == recall_idx)[0]\n",
    "                results = results.append({\n",
    "                    'M': M,\n",
    "                    'efConstruction': efConstruction,\n",
    "                    'efSearch': efSearch,\n",
    "                    'recall@1': recall,\n",
    "                    'build_time': build_time,\n",
    "                    'search_time': search_time,\n",
    "                    'memory_usage': memory_usage\n",
    "                }, ignore_index=True)\n",
    "            del index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "results.to_csv('./results.csv', sep='|', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>M</th>\n",
       "      <th>efConstruction</th>\n",
       "      <th>efSearch</th>\n",
       "      <th>recall@1</th>\n",
       "      <th>build_time</th>\n",
       "      <th>search_time</th>\n",
       "      <th>memory_usage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>205773.0</td>\n",
       "      <td>1489.0</td>\n",
       "      <td>547995894.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>205773.0</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>547995894.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>205773.0</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>547995894.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>205773.0</td>\n",
       "      <td>3473.0</td>\n",
       "      <td>547995894.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>205773.0</td>\n",
       "      <td>2977.0</td>\n",
       "      <td>547995894.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     M  efConstruction  efSearch  recall@1  build_time  search_time  \\\n",
       "0  2.0             2.0       2.0      32.0    205773.0       1489.0   \n",
       "1  2.0             2.0       4.0      41.0    205773.0       1984.0   \n",
       "2  2.0             2.0       8.0      47.0    205773.0       1984.0   \n",
       "3  2.0             2.0      16.0      59.0    205773.0       3473.0   \n",
       "4  2.0             2.0      32.0      65.0    205773.0       2977.0   \n",
       "\n",
       "   memory_usage  \n",
       "0   547995894.0  \n",
       "1   547995894.0  \n",
       "2   547995894.0  \n",
       "3   547995894.0  \n",
       "4   547995894.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "results = pd.read_csv('./results.csv', sep='|')\n",
    "results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for efConstruction in [2, 16, 64]:\n",
    "    sns.lineplot(data=results[results['efConstruction'] == efConstruction], x='efSearch', y='recall@1', hue='M')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/s0/_jtvm_x17pz3grlnzl2j8k6r0000gn/T/ipykernel_23753/88108748.py:4: UserWarning: Attempted to set non-positive bottom ylim on a log-scaled axis.\n",
      "Invalid limit will be ignored.\n",
      "  plt.ylim(0, 500_000)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/s0/_jtvm_x17pz3grlnzl2j8k6r0000gn/T/ipykernel_23753/88108748.py:4: UserWarning: Attempted to set non-positive bottom ylim on a log-scaled axis.\n",
      "Invalid limit will be ignored.\n",
      "  plt.ylim(0, 500_000)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/s0/_jtvm_x17pz3grlnzl2j8k6r0000gn/T/ipykernel_23753/88108748.py:4: UserWarning: Attempted to set non-positive bottom ylim on a log-scaled axis.\n",
      "Invalid limit will be ignored.\n",
      "  plt.ylim(0, 500_000)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for efConstruction in [2, 16, 64]:\n",
    "    sns.lineplot(data=results[results['efConstruction'] == efConstruction], x='efSearch', y='search_time', hue='M')\n",
    "    plt.yscale('log')\n",
    "    plt.ylim(0, 500_000)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='M', ylabel='memory_usage'>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(data=results, x='M', y='memory_usage')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13 (default, Mar 28 2022, 06:59:08) [MSC v.1916 64 bit (AMD64)]"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "5fe10bf018ef3e697f9035d60bf60847932a12bface18908407fd371fe880db9"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
